As the scope of data analytics and business intelligence automation increases, the significance of data quality improvement rises. Enterprises recognize how database errors or outdated records threaten insight extraction’s practical worth. Therefore, they demand strategies and tools to address poor data quality issues. This post will elaborate on the role of data quality management in ensuring reliable and accurate information.
What is Data Quality Management?
Data quality management (DQM) involves inspecting, planning, and maintaining target data quality standards. So, several consultants and data operations solutions develop goal-driven DQM vocabulary and guidelines. They address the on-premise data centers alongside more contemporary cloud integration workflows.
A data quality manager must supervise all processing practices and encourage stakeholders to embrace database integrity, validity, and completeness. Moreover, the other three requirements across global DQM standards focus on accuracy, uniqueness, and timeliness.
The Six Dimensions of Data Quality Management
1| Accuracy
Data accuracy and correctness reflect realistic event dynamics. If gathering precise first-hand business intelligence is expensive, organizations can mine the authoritative resources across the web. Alongside sourcing data, accuracy governs how data represents reality throughout the lifecycle, from collection to deletion.
2| Completeness
Empty records create inconsistent visual reports, skew insight discovery efforts, and undermine stakeholders’ faith in data collection technologies. Therefore, data lifecycle management solutions employ extensive automation to estimate the best strategies for addressing null values in databases. Sometimes, geometric extension or standard interpolation methods are insufficient. So, DQM specialists must integrate machine learning (ML) models to predict unavailable data points.
3| Integrity
Data integrity can be referential or relationship-based. If one team defines two variables in a function to correlate, others must respect this declaration irrespective of industry application. Otherwise, one database will contradict another department’s intelligence archives. As a result, your employees might waste resources on finding a more acceptable version of the relationship. DQM strategists recommend finalizing references and relationships while preventing contradictory revisions to either.
4| Uniqueness
Duplicate data entries consume valuable data storage resources and increase operating costs. They can also convolute vital statistical functions, like standard deviation, tolerance threshold, and variance. Uniqueness in data quality management helps decrease duplication in datasets for reliable and accurate information extraction.
5| Timeliness
Timeliness demands reducing delays between event occurrence and data availability. Although instantaneous data capturing is unrealistic, implementing modern IT technologies must assist in accelerating event-driven reporting. For instance, animated dashboards offer visual descriptions of actual changes in the target system. The transportation department will use it differently than medical professionals. However, both domains can enhance decision-making if they get real-time visual trends. Therefore, the significance of timeliness in DQM depends on industry use cases.
6| Validity
Social networks, popular magazines, or documentaries based on insufficient material might provide a one-sided perspective. Likewise, a key opinion leader (KOL) in a sector might have failed to keep up with the latest research and development projects. As such, validating the acquired data becomes necessary. Invalid database entries can cause severe harm to your enterprise’s performance reporting, business development strategy, and revenue projections. So, managers must identify the most authoritative and up-to-date resources that help throughout pre-analysis validation.
Tools for Data Quality Management
- Ataccama ONE offers a centralized graphical user interface (GUI) facilitating master data management, DQM functions, and governance support. This platform also embraces modular integration approaches. So, your teams can choose what they require instead of bloating their systems with unnecessary features.
- IBM InfoSphere ensures data integrity across supplier networks, client relations, and geolocation tagging for shipments.
- Oracle has an enterprise data quality or EDQ platform that excels at efficient DQM compliance and database optimization.
- Cloudingo, a Salesforce app exchange partner, has evolved from DupeCatcher by Symphonic Source. Therefore, it has augmented its data duplication removal capabilities, presenting versatile data quality management tools.
- Syniti helps coordinate master data management (MDM), covering multiple data origins. It also empowers brands to migrate data between Oracle, Salesforce, and Snowflake.
Conclusion
According to CambridgeSpark’s knowledge base, poor data quality hurts brand reputation due to the corresponding decrease in the reliability of critical business decisions. Additionally, ineffective DQM implementation might result in significant time loss and reporting errors. Therefore, poor dataset quality has unforeseeable hidden costs, limiting your competitiveness.
Thankfully, the world has ISO 8000 since its 2009 approval, a well-recognized global standard guiding stakeholders in data quality and enterprise master data operations. It describes the key MDM concepts, including quality identifiers, authoritative legal entity identifiers (ALEIs), SmartPrefix, and portability.
However, data quality needs vary from organization to organization, suggesting companies require unique DQM strategies and technologies to embrace ISO 8000. Therefore, brands collaborate with experienced professionals in data quality management to ensure reliable, accurate, and business-relevant information. Doing so helps eliminate database inconsistencies and promote responsible data operations.
